Abstract

'Doctus ' 1 is capable of deduction also called rule-based reasoning and of induction, which is the symbolic version of reasoning by cases 2. If connected to databases or data warehouses the inductive reasoning of Doctus is also used for data mining. To handle numerical domains Doctus uses statistical clustering algorithm. We define the problem in three steps: how to perform a clustering, which is neither rigid nor sensitive to noise, benefiting from the properties of the application domain, reducing the complexity as much as possible, and supplying the decision maker with useful information enabling the possibility of interaction? In this paper we present the conception of Automated Fuzzy-Clustering using triangular and trapezoidal Fuzzy-sets, which provides overlapping Fuzzy-set covering of the domain. I. FUZZY CLUSTERING FOR SYMBOLIC ES - WHY? We investigate the expert systems in supporting the business decision making process. Let's first examine the domain of the application, to map characteristics that are important to choose the appropriate tool for support. We are dealing with decision making of a leader and of a manager on the expert level of knowledge and higher, who are to considering much of soft information and hard data, and use heuristic processes to take the decisions. First there is a need to discover the properties of the heuristic processes in comparison to other processes: 1. At deterministic processes there is an expected value only with no dispersion. It is determined what output follows a particular input, it will happen in 100 % of repetitions. Small changes on the input will result in small changes on the output, which can be calculated precisely. Deterministic processes can be met e.g. in classical physics (mechanics of not microscopic, but also not astronomy sized bodies).